Abstract
Latent low-rank representation has been applied to multi-level image decomposition for the fusion of infrared and visible images to obtain good results. However, when the original infrared and visible images are of low quality, the visual effect of the fused images is still unsatisfactory. To combat this challenge, this paper proposes an infrared and visible image fusion method based on multi-level latent low-rank representation joint with image enhancement and multiple visual weight information. First, the source images are decomposed into detail parts - including detail images and detail matrices - and the base images respectively using multi-level latent low-rank representation. Then the nuclear norm based fusion strategy is used to fuse the detail matrices and multi-visual weights determined by the clarity, local contrast and edge-corner saliency is used to fuse the detail images. The aforementioned two fusion results are weight averaged to obtain a fused detail image. The base images are fused by an averaging strategy after Retinex-based enhancement. The final fused image is obtained by combining the fused detail image and the fused base image. Compared with other state-of-the-art fusion methods, the proposed algorithm displays better fusion performance in both subjective and objective evaluation.
Highlights
I MAGE fusion is an important branch of multi-sensor information fusion
The infrared and visible images were decomposed by multilevel Latent low-rank representation (LatLRR) to extract detail parts and base parts of the input images at several representation levels
The clarity, local contrast and edge and corner significance of the image were calculated to construct the visual weights for deal parts fusion, the final fusion results of the detail images are weigh averaged by nuclear norm fusion and multiple visual weights fusion
Summary
I MAGE fusion is an important branch of multi-sensor information fusion. It aims to integrate the image information of a certain moment under the same scene obtained by different types of sensors, which aids in describing the characteristics of the target scene more comprehensively [1]. The imaging of infrared images occurs by detecting the thermal radiation of the object itself, which highlights the thermal target blocked by the object. This technique reduces interference by environmental factors, but its texture detail information is insufficient and the contrast is low [2]. The fused image contains highly-detailed texture information and clear infrared thermal radiation targets, which is conducive to further target detection and other work[3,4]
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